Founded in 2002, the Brain Connectivity Workshop (BCW) is an annual international meeting for in-depth discussions of all aspects of brain connectivity research. By bringing together experts in computational neuroscience, neuroscience methodology and experimental neuroscience, it aims to improve the understanding of the relationship between anatomical connectivity, brain dynamics and cognitive function. These workshops have a unique format, featuring only short presentations followed by intense discussion. This year’s workshop is co-organised by Wellcome, putting the spotlight on brain connectivity in mental health disorders. We look forward to having you join us for this exciting, thought-provoking and inclusive event.

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

This course provides a fundamental foundation in the modern techniques of experimental neuroscience. It introduces the essentials of sensors, motor control, microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot. In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.

Artificial neural networks simplify complex biological circuits into tractable models for computational exploration and experimentation. However, the simplification of artificial models also undermines their applicability to real brain dynamics. Typical efforts to address this mismatch add complexity to increasingly unwieldy models. Here, we take a different approach; by reducing the complexity of a biological cortical culture, we aim to distil the essential factors of neuronal dynamics and plasticity. We leverage recent advances in growing neurons from human induced pluripotent stem cells (hiPSCs) to analyse ex vivo cortical cultures with only two distinct excitatory and inhibitory neuron populations. Over 6 weeks of development, we record from thousands of neurons using high-density microelectrode arrays (HD-MEAs) that allow access to individual neurons and the broader population dynamics. We compare these dynamics to two-population artificial networks of single-compartment neurons with random sparse connections and show that they produce similar dynamics. Specifically, our model captures the firing and bursting statistics of the cultures. Moreover, tightly integrating models and cultures allows us to evaluate the impact of changing architectures over weeks of development, with and without external stimuli. Broadly, the use of simplified cortical cultures enables us to use the repertoire of theoretical neuroscience techniques established over the past decades on artificial network models. Our approach of deriving neural networks from human cells also allows us, for the first time, to directly compare neural dynamics of disease and control. We found that cultures e.g. from epilepsy patients tended to have increasingly more avalanches of synchronous activity over weeks of development, in contrast to the control cultures. Next, we will test possible interventions, in silico and in vitro, in a drive for personalised approaches to medical care. This work starts bridging an important theoretical-experimental neuroscience gap for advancing our understanding of mammalian neuron dynamics.

Remarkable advances in experimental neuroscience now enable us to simultaneously observe the activity of many neurons, thereby providing an opportunity to understand how the moment by moment collective dynamics of the brain instantiates learning and cognition. However, efficiently extracting such a conceptual understanding from large, high dimensional neural datasets requires concomitant advances in theoretically driven experimental design, data analysis, and neural circuit modeling. We will discuss how the modern frameworks of high dimensional statistics and deep learning can aid us in this process. In particular we will discuss: (1) how unsupervised tensor component analysis and time warping can extract unbiased and interpretable descriptions of how rapid single trial circuit dynamics change slowly over many trials to mediate learning; (2) how to tradeoff very different experimental resources, like numbers of recorded neurons and trials to accurately discover the structure of collective dynamics and information in the brain, even without spike sorting; (3) deep learning models that accurately capture the retina’s response to natural scenes as well as its internal structure and function; (4) algorithmic approaches for simplifying deep network models of perception; (5) optimality approaches to explain cell-type diversity in the first steps of vision in the retina.

Remarkable advances in experimental neuroscience now enable us to simultaneously observe the activity of many neurons, thereby providing an opportunity to understand how the moment by moment collective dynamics of the brain instantiates learning and cognition.  However, efficiently extracting such a conceptual understanding from large, high dimensional neural datasets requires concomitant advances in theoretically driven experimental design, data analysis, and neural circuit modeling.  We will discuss how the modern frameworks of high dimensional statistics and deep learning can aid us in this process.  In particular we will discuss: how unsupervised tensor component analysis and time warping can extract unbiased and interpretable descriptions of how rapid single trial circuit dynamics change slowly over many trials to mediate learning; how to tradeoff very different experimental resources, like numbers of recorded neurons and trials to accurately discover the structure of collective dynamics and information in the brain, even without spike sorting; deep learning models that accurately capture the retina’s response to natural scenes as well as its internal structure and function; algorithmic approaches for simplifying deep network models of perception; optimality approaches to explain cell-type diversity in the first steps of vision in the retina.